Encouraging reactivity to create robust machines

    Authors

    J. Lehman; S. Risi; D. D'Ambrosio;K. O. Stanley

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Adm. Soc.

    Keywords

    EVOLUTIONARY ROBOTICS; NEURAL-NETWORKS; REALITY GAP; CONTROLLER; SIMULATION; Computer Science, Artificial Intelligence; Psychology, Experimental; Social Sciences, Interdisciplinary

    Abstract

    The robustness of animal behavior is unmatched by current machines, which often falter when exposed to unforeseen conditions. While animals are notably reactive to changes in their environment, machines often follow finely tuned yet inflexible plans. Thus, instead of the traditional approach of training such machines over many different unpredictable scenarios in detailed simulations (which is the most intuitive approach to inducing robustness), this work proposes to train machines to be reactive to their environment. The idea is that robustness may result not from detailed internal models or finely tuned control policies but from cautious exploratory behavior. Supporting this hypothesis, robots trained to navigate mazes with a reactive disposition prove more robust than those trained over many trials yet not rewarded for reactive behavior in both simulated tests and when embodied in real robots. The conclusion is that robustness may neither require an accurate model nor finely calibrated behavior.

    Journal Title

    Adaptive Behavior

    Volume

    Adapt. Behav.

    Issue/Number

    6

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    484

    Last Page

    500

    WOS Identifier

    21

    ISSN

    1059-7123

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